Anne Hendricks Bass Born Anne Hendricks ( 1941-10-19) October 19, 1941 (age 76), U.S. Residence 960 Fifth Avenue, Education Alma mater Occupation Documentary filmmaker, philanthropist, art collector Net worth US$690 million (2000) Political party Democrat Spouse(s) (divorced) Children Samantha Bass Relatives (son-in-law) Anne Hendricks Bass (born October 19, 1941) is an American investor, documentary filmmaker, philanthropist and art collector. She is the former wife of billionaire oilman. She directed the 2010 documentary film Dancing Across Borders. She is a patron of the arts in and. Contents.
Early life Anne Hendricks was born on October 19, 1941 in, Indiana. Her father was a 'successful Indianapolis surgeon' and urologist. Her mother, a graduate of, was a 'golf-champion mother'. She has younger sisters and a brother. She was educated in public schools in Indianapolis until 1957, when she transferred to the Tudor Hall School for Girls, a private girls' school in Indianapolis now known as the, graduating in 1959. She took ballet lessons as a child.
She graduated from in 1963, where she majored in Italian literature. Career After graduation, she was an executive trainee at, where she worked as an associate buyer. She later became a contributing editor. Through her divorce settlement, Bass became the owner of over one million shares of.
She has been on the list since 1989. She was worth an estimated US$690 million in 2000. Bass directed Dancing Across Borders, a documentary about dance released in February 2010. The documentary shows how Bass sponsored a teenager from to attend the and become a professional ballet dancer for the. The film was shown at the in Manhattan.
Suggested the documentary lacked 'an objective voice,' as Bass was the one directing and producing a film showcasing her goodwill. Philanthropy and art collection Bass volunteered for the of Fort Worth. She supported the and the. She also supported the, which she 'rescued from bankruptcy'. She donated US$300,000 on her own, complemented by a US$250,000 donation from the Sid Richardson Foundation. Additionally, she supported the.
She made charitable contributions to the, where she helped with the landscaping of the grounds. She served on the committee of the Jewel Charity Ball, benefiting the in Fort Worth, Texas. Bass served on the International Council of the in New York City. From 1980 to 2005, she served on the Board of Trustees of the. She also supported the. Additionally, she has taken trips with the.
Bass collects paintings by, and. She is the owner of by Picasso. Personal life Bass met her first husband, a billionaire heir to a Texas oil fortune, at a birthday party when Anne was visiting her cousins in Fort Worth; she was only nine years old. They started dating in college. Their wedding was held on June 26, 1965 in a Presbyterian church in Indianapolis, followed by a reception at the Woodstock Country Club. They honeymooned in Europe. After living in for a year and for two years, they moved into a estate overlooking the.
Later, they moved into a mansion on Deepdale Drive, designed in 1970 by architect with grounds designed by British landscape architect. They also lived in an apartment on overlooking in, designed. Sid and Anne had two daughters:, an author, and Samantha Sims Bass, a photographer. When they divorced in 1988, she received US$200 million in the settlement, which was the largest ever in the state of Texas. She decided to keep her former husband's name. Bass resides in the 960 Fifth Avenue apartment she received in her divorce settlement, as well as the Rudolph- designed home in Westover Hills.
She also owns a 1,000-acre estate in South Kent, with her boyfriend, who is a painter. In 2007, they were both held hostage at the estate. Five years later, in 2012, her Romanian-born butler was sentenced to 20 years in prison for the hostage situation, when he attempted to extort millions from Bass.
Bass was described as 'relentlessly private'. She enjoys reading novels. References. October 9, 2000. Retrieved October 2, 2015.
^ Davidson, John (February 1987). Texas Monthly. Pp. 80–83; 130–133. Retrieved October 5, 2015.
^ Curtis, Charlotte (April 24, 1984). New York Times. Retrieved October 5, 2015. Texas Monthly. Retrieved October 5, 2015. ^ Reginato, James (January 2010).
Retrieved October 2, 2015. ^ Catsoulis, Jeannette (March 25, 2010). The New York Times. Retrieved October 2, 2015.
^ Rovzar, Chris (March 22, 2010). New York Magazine. Retrieved October 2, 2015. The Galveston Daily News.
Galveston, Texas. March 30, 1987. Retrieved October 2, 2015 – via.
Horowitz, Jason (December 26, 2005). The New York Observer. Retrieved October 2, 2015. ^ Callahan, Maureen (October 16, 2011). The New York Post.
Retrieved October 2, 2015. The Victoria Advocate.
October 30, 1988. Retrieved October 2, 2015. Talley, Andre Leon (July 7, 2009). Retrieved October 2, 2015. Warren, Lydia (June 18, 2012). The Daily Mail.
Retrieved October 2, 2015. The Daily Mail. March 18, 2011. Retrieved October 2, 2015. The New York Post. August 17, 2012. Retrieved October 2, 2015.
Moving Day is Here Your Sims are moving into a brand-new apartment, and their lives will never be the same! Adventure, fun, and drama await them as they meet new people and explore their new neighborhood. Will they take their kids to the local playground, mingle in coffee shops, or hit the park to learn from the break-dancers?
Close quarters mean new opportunities-move in with compatible roomies for a thriving social life, advance your career with the right social network, or find true love just down the hall. Whether they live in artsy converted lofts, the ultimate studio bachelor pads, or luxury apartments with their own butler, Your Sims will experience all of the excitement of apartment life! Key Features Move into the perfect apartment: a spacious loft, a cozy place for a young couple, or a multi-bedroom flat-share with friends. Mingle with Sims from all-new social groups: stylish socialites, artsy bohemians, sports jocks, gadget-collecting techies, or edgy gearheads.
Take advantage of apartment life: form social networks to make new friends, advance their careers, or look for love. Build your Sim’s new reputation meter: with a good reputation, your Sim can find the right friends to help them achieve their goals! Control multiple households: will you make your Sims live in happy harmony or comical conflict?
System requirements: Windows OS: Windows Vista, XP, ME, 98, 2000 CPU: 1.8 GHz (2.4 GHz for Vista) RAM: 512 MB 1 GB for Vista Disc drive: 8x CD-ROM/DVD drive HDD: 1.5 GB free space Video: 64 MB DirectX 9.0 and T&L compatible Sound: DirectX 9.0c compatible If you want to install The Sims 2 and all the expansion and stuff packs, you must install them in this order. The Sims 2 - The Sims 2: Apartment Life (Expansion) This is just the Apartment Life expansion. Don't forget to get the core game if you haven't already. This has a crack, patch, and keygen.
Methods An expert panel identified survey items within each MOVES domain from the Canadian Community Health Survey- Healthy Aging Cycle (2008–2009) for 28,555 (weighted population n = 12,805,067) adults (≥45 years). We refined MOVES using principal components analysis and Cronbach’s alpha and weighted items so each domain was 10 points. Expected mobility trends, as assessed by average MOVES, were examined by sociodemographic and health factors, and by province, using Analysis of Variance (ANOVA). While the pace and pattern of population shifts differ across the world, the older population is increasing globally.
In North America the proportion of the population 65 years and older is expected to rise from 12.8% in 2008 to 20.8% in 2040. This unprecedented shift demands that systems and communities meet needs of this aging demographic. Mobility restrictions influence older adult independence , constrict community engagement , , and increase negative health outcomes and premature mortality ,.
Thus it is imperative that we devote collective attention to strategies and tools that support maintaining mobility later in life. Mobility is multi-dimensional and includes the importance of social and community engagement, use of transportation, and cognition ,. The Canadian Institute for Health Research (CIHR) acknowledged this broader definition of mobility; in the Mobility in Aging Strategic Initiative (CIHR Institute of Aging) mobility was defined as encompassing participation in society, as well as the ability to drive and access public transportation. In the transportation realm, mobility is often measured as trip rate (any mode). In addition, transportation studies recognized that mobility should include one of the following dimensions: 1) access to places of desire (such as visiting family or friends), 2) psychological benefits of travel (either social contact or independence), or 3) benefits of physical movement itself and potential travel ,.
Inquisition
Urban planning recognized community environments as important in shaping mobility ,. Understandably, advocacy groups focused on the role neighborhoods play in maintaining independence and mobility for older adults ,. Methods used to assess mobility vary across research studies and fields. However, existing metrics often focus on an individual’s capacity for, or enacted physical function. Cognitive ability to engage, social connections with an older person’s community, or transportation choices are most often excluded from these metrics. Current measures of mobility include assessments of transfer skills, gait, or wheelchair mobility ,.
Activities of daily living (ADL) and instrumental activities of daily living (IADL) are also used to assess mobility clinically ,. These methods were criticized as failing to capture what people actually do in their daily lives or how an individual is involved in social situations. Life-space measures attempt to capture broader mobility, by including mobility inside the home, outside the home, within the neighborhood, and beyond. Yet the life-space measure does not capture transportation patterns or community engagement of older adults directly. Given the expanding definition of mobility, and the importance of mobility for older adults, there is a need for measures of mobility that encompass these domains. Therefore, we respond to both the opportunity and need for a holistic measure of older adult mobility that includes physical, cognitive, social, and transportation domains.
Thus the objectives of our study were twofold: 1) to create a Mobility Over Varied Environments Scale (MOVES) using a large, population based study of Canadian older adults, and 2) to apply MOVES to examine the distribution of mobility across sociodemographic and health characteristics of the Canadian population. This second objective allows us to examine the performance of MOVES. For this, we hypothesize that MOVES will follow known patterns of mobility, including lower mobility for Canadians in worse health, at older ages, or with lower socioeconomic status. Conceptual frameworks MOVES draws on the comprehensive mobility framework outlined by Webber et al. and the World Health Organization’s International Classification of Functioning, Disability, and Health (ICF).
Webber et al. Defined mobility broadly as “the ability to move oneself (e.g., by walking, by using assistive devices, or by using transportation) within community environments that expand from one’s home, to the neighborhood, and to regions beyond.” This framework acknowledges that mobility takes many forms, including walking, using a wheelchair, driving, and using alternate forms of transportation. The Webber framework identifies five key domains that determine older adult mobility: physical, cognitive, psychosocial, environmental, and financial. These domains are interrelated. For example, an individual’s physical impairments (physical) with or without accompanying psychological factors (e.g. Depression) can contribute to the development of fear of falling (cognitive), leading to activity restriction and reduced social engagement (psychosocial).
Similarly, the ICF has a broad description of mobility that captures both indoor and outdoor movement as well as the use of assistive devices and transportation. Further, the description includes participation in activities and environmental factors that play a role in mobility. 1 Iterative process to create the Mobility Over Varied Environment Scale (MOVES). Dotted lines indicate the involvement of an expert panel of qualitative and quantitative researchers who played three key roles: 1) helping to synthesize the mobility frameworks 2) selecting specific items based on questions identified in CCHS-HA and 3) establishing guiding principles for the creation of MOVES that were used to select specific items in CCHS-HA. Note that the creation of MOVES was primarily based in conceptual frameworks and then underwent statistical refinement to both confirm frameworks and tailor the MOVES measure. A sensitivity analysis was run including all items based on frameworks (had barriers and limitations within each domain as well as an additional financial domain) Concept-based MOVES creation An expert panel of researchers and staff ( n = 10) from gerontology, epidemiology, family medicine, transportation, and health behavior played a critical role in item selection.
First, they helped synthesize existing mobility frameworks. Second, after two researchers separately identified items from the Canadian Community Health Survey- Healthy Aging (CCHS-HA) that related to the mobility frameworks, the expert panel determined which items to include.
Statistical refinement On the selected items, we ran Cronbach’s alpha and a confirmatory Principal Component Analysis (PCA) to determine whether: 1) items were contributing to their respective domains and the overall score, 2) the items grouped together as anticipated, and 3) what proportion of variance was explained by these items. Items were the combined into a final MOVES. Understanding canadian mobility using MOVES We applied MOVES to the CCHS-HA to better understand the distribution of mobility in the Canadian population. The CCHS-HA is a cross-sectional survey ( n = 30,865) of the Canadian population living in the 10 provinces across Canada (Canadian territories were excluded). Details can be found elsewhere. Briefly the Healthy Aging component was completed December 2008 through November 2009 and surveyed people (≥ 45 years) using computer assisted personal interviewing (94% of interviews conducted in person) achieving an overall response rate of 74.4%.
For the creation of MOVES, we included CCHS-HA participants who had all component items that comprised MOVES (final n = 28,555). Weighted frequencies were used to describe the sociodemographic characteristics of the CCHS-HA sample.
MOVES mean score was examined across each sociodemographic characteristic. We obtained p-values for comparisons across categories from t-tests and analysis of variance (ANOVA). MOVES mean score was also compared across age and gender within each province. We weighted all results using the Statistics Canada proportional sampling scheme and applied Balanced Repeated Replication (BRR) with 500 bootstrap weight variables to obtain the correct standard errors for ANOVA.
All analyses were conducted using SAS, version 9.4 (SAS Institute Incorporated: Cary, NC). Results- moves creation Item selection To select items, the expert panel established four guiding principles: 1) MOVES should focus on actualized or realized mobility of an individual, rather than potential for mobility (e.g. How often one engages in community activities versus whether community activities exist), 2) if there were existing metrics within a domain, these metrics should remain intact, rather than being split into their component parts, 3) where possible, MOVES should be an absolute rather than a relative metric, to be applicable beyond the Canadian population, and 4) items should represent components, rather than outcomes, of mobility (e.g. Loneliness was excluded as it may result from low social engagement).
MOVES domains In practice, the measurement of Webber’s psychosocial domain and cognitive domain overlap. Therefore, to develop MOVES we modified the psychosocial domain to be primarily social, based on the complementary domain from the ICF, “activities and participation.” This domain includes interpersonal interactions and relationships, as well as community social and civic life. Similarly, many of the environmental determinants in both Webber and ICF models are related to service systems and policies that influence transportation mode.
Therefore, this domain was conceptualized more narrowly in our work as “transportation.” Physical. Our expert panel identified eight items (five of which were barriers or limitations) to include in the physical domain (Table ). We used activities of daily living (ADL), ambulation, and physical activity items to capture physical function and activity. ADL items excluding meal preparation come from the Older Americans Resources and Services (OARS) Multidimensional Functional Assessment Questionnaire© (OMFAQ). Ambulation items were from the adapted version of the Health Utilities Index (HUI) mark 3 , a validated instrument which provides a description of an individual’s overall functional health.
Because sedentary behavior and physical activity independently predict successful aging , physical activity was measured using the Physical Activity Scale for the Elderly (PASE), a validated and copyrighted instrument (1991) developed by the New England Research Institutes (NERI) to provide an overall assessment of self-reported occupational, household and leisure activities over the past seven days in older persons. Barriers and limitations included reporting a health condition limiting participation in activities, public transportation use, or health improvements. AAll component items coded so that higher points indicate more positive mobility and then scaled to be between 0 and 10 points. Barriers each coded as penalties of 1 point bStatistical refinement using Cronbach’s alpha identified that barrier and limitation items (including all of those in the financial domain) were not adding to the overall MOVES or the domains. These items only used in sensitivity analyses cNumber of modes was used for the MOVES, but the breakdown of each transport mode is also presented for descriptive purposes dThe Financial Domain was not included in the final MOVES due to results of the statistical refinement Cognitive In the psychological and cognitive domain, we used two items, one for cognition and one that measured fear of falling. Cognition was captured with the HUI cognitive health status.
This measures whether a respondent can remember most things, think clearly, and solve day-to-day problems. We used fear of falling to tap into self-efficacy around mobility. A survey item related to fear of falling was administered to all those 65 years or older (response categories: not worried or concerned, worried or concerned but haven’t stopped activities, and worried or concerned and have stopped activities). Transportation Transportation was measured using four items, one represented travel mode of the respondent and three reported transportation-related barriers and limitations.
For travel mode, participants answered the question, “in the past month, which of the following (other) forms of transportation have you used?” Respondents were given the options: passenger in a motor vehicle; taxi; public transportation such as bus, rapid transit, subway or train, accessible transit, cycling, walking, wheelchair or motorized cart, or none. Barriers and limitation included reporting transportation problems that limited their participation or ability to improve their health. Social Social aspects of mobility were measured using three items: a sense of belonging to the local community; frequency of participation in community activities; and tangible social support. Sense of belonging was measured by asking respondents “How would you describe your sense of belonging to your local community? Frequency of community-related activity participation was assessed by participation in any type of community-related activity during the previous 12 months and then categorized as participation once a year, once a month, once a week, or once a day. Tangible social support was taken from the Medical Outcomes Study (MOS) Social Support Survey. This scale ranges from 0 to 16 and was not asked during proxy interviews; therefore proxy respondents do not have a MOVES score.
Financial The expert panel identified that an individual’s financial standing influences and interacts with the other domains. However, since income or wealth are not actualized mobility, we only included financial markers of whether an individual felt cost prohibited them from being mobile or engaging with their community (barriers and limitations). Ultimately, this domain was not included in MOVES due to findings during the statistical refinement process described below. Statistical refinement, scoring and compilation We ran Cronbach’s alpha to determine internal consistency for all items (0.61) and within each domain (range from 0.11–0.64). By examining Cronbach’s alpha if each item were deleted, we identified that MOVES performed equally well without barrier and limitation items (including all those in the financial domain).
After removing these items, the final MOVES standardized Cronbach’s alpha was 0.58. The cognitive domain had the lowest internal consistency, likely because cognitive function and fear of falling tap into different, yet related, elements of mobility-related cognition or psychology. We ran PCA both on all items identified by the expert panel and just on items remaining after Cronbach’s alpha analysis. We ran these PCA with no restrictions placed on number of factors as well as with factor constraints equal to the number of domains. In general, items grouped within the anticipated domains. However, fear of falling loaded onto both the cognitive and physical domain and transportation mode loaded onto a number of factors. This cross-over between factors was expected, as theoretical frameworks include interconnectedness between domains.
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PCA also confirmed we should restrict to only four domains; in the solution including all items, the first five factors accounted for only 39.1% of variance (all five had eigenvalues greater than 1). In the solution using only the subset of items indicated by Cronbach’s alpha, the first four factors accounted for 62.3% of the variance (only the first three had eigenvalues greater than 1). Thus, statistical refinement using PCA confirmed that removing barriers and limitations (including the entire financial domain) from MOVES created an equally sound score.
We provide final items and scoring for items in MOVES in Table. All items (except PASE) were categorical and were left in their original metrics based on the guiding principle for absolute versus relative items. Scores were recoded so higher values indicate greater mobility and then were scaled to 10 points. As recommended by Statistics Canada , PASE data were used as quartiles. Since respondents aged under 65 were not asked about their fear of falling, we allocated them 10 points.
We chose to allocate points based on the number of transportation modes each respondent reported. We did not prioritize active mode, aligned with the conceptual frameworks that considered all forms of transportation as important to mobility.
We grouped transportation modes as: driving oneself (having a driver’s license and driving at least once in the previous month), being driven (being a passenger or taking a taxi), taking public or accessible transit (where accessible transit included service designed for persons with disabilities or mobility issues), and active transit (walking or cycling for transportation). Items within each domain were averaged, so each domain received an equal weight of 10 points. The final MOVES was created by summing across four domains for a possible score of 0 to 40. Sociodemographic or Economic Characteristics Weighted Percentage (95% CI) Mean MOVES (95% CI) p-value for MOVES a Sex.
Health Characteristics Weighted Percentage (95% CI) Mean MOVES (95% CI) p-value for MOVES a Receipt of Home Care. Within the 28,555 adults with complete data to create MOVES, the 10th percentile of MOVES was 24.2 (95% confidence interval (CI) 24.0, 24.4) and the 90th percentile was 34.7 (CI 34.6, 34.9), with a mean of 29.6 (CI 29.5, 29.7). Scores were generally high within each MOVES domain, although differences existed in each domain by age (Figure ). Out of 10, Canadians scored a mean physical mobility of 8.1 (95% CL 8.1, 8.1), mean cognitive mobility of 9.0 (95% CL 9.0, 9.1), and mean social mobility of 7.1 (95% CL 7.0, 7.1). Over 90% used between one and three transportation modes, giving a mean transportation mobility score of 5.2 (95% CL 5.2, 5.3). MOVES was higher for those who were younger, male, white, better educated, employed, higher income, married, home owners, born in Canada, and living in larger urban areas (Table ). Higher MOVES was also associated with healthier behaviors and better health outcomes (Table ).
Those with excellent self-perceived health had an average MOVES of 31.2 (CI 31.0, 31.4), compared with those with poor self-perceived health, who had an average MOVES of 24.0 (CI 23.5, 24.4). Lower values for MOVES by age were statistically significantly different for males and females ( p.
We used data from a large, population-based study to create a comprehensive measure of mobility, MOVES, that encompasses multiple domains of actualized mobility for mid- to late-life adults living in the community. Grounded in evidence and conceptual frameworks, and refined using input from experts and statistical analysis, MOVES captures the complexity inherent in mobility, including physical, cognitive, social, and transportation domains. Across the representative sample of Canadian older adults, MOVES aligns with expected mobility patterns (higher for those who were younger, higher socioeconomic status, and in better health). The creation of a holistic mobility score bridges gaps between other classification systems, as it better captures where people go, what they do in their daily lives, and their social connections to others.
In contrast to typical clinical measures that focus on physical capacity , MOVES provides researchers, practitioners, and policy-makers the opportunity to evaluate actualized mobility more broadly. Particularly noteworthy is our inclusion of transport modes. Older adults out-of-home activity levels decrease with driving cessation , and cessation of driving was associated with worse health outcomes , although directionality of these associations is unclear. Thus, including both automobile use and transportation alternatives was critical to characterizing older adult mobility. Another novel component of MOVES is its ability to capture social engagement and mobility through tangible social support, sense of belonging, and frequency of participation in community events.
Links between social support, health, and overall mortality have been well documented , , giving further credence to the importance of including social connections and community participation in a mobility score. The sociodemographic and economic patterns we observed in MOVES align with previous literature on older adult activity. As expected, mobility declines with age. MOVES is higher for men, and declines over age were steeper for women than for men. This differential decline is consistent with reports of ADL in older women , and may be due to smaller support networks due to employment patterns, or potential differences in driving. However, gender differences could also result from survivor bias as studies of functional decline show men as less likely to survive , possibly resulting in a select group of stronger, more mobile males at older ages.
Lower MOVES for those with lower income, education, employment, and home ownership, are consistent with evidence on the role of socioeconomic status in functional status , chronic disease , and mortality. However, there remains controversy about the mechanisms linking socioeconomic status to mobility. Income and wealth may factor into neighborhood choices, providing fewer options for lower socioeconomic adults. Similarly, educational or occupational differences may afford disparate out of home engagement opportunities or access tools to cope with declines in physical functioning. Interestingly, we observed higher levels of mobility for Canadians living in larger urban areas.
This highlights the need for continued research to differentiate between needs of older adults in rural versus urban centres, and the need to address rural seniors’ health needs ,. Alternatively, larger-scale geographic patterns by region may be more useful as descriptive distributions of mobility for resource allocation and health care service provision (which is under provincial jurisdiction in Canada). Not surprising, we found those with higher MOVES had better health outcomes, including self-perceived health, life satisfaction, and fewer chronic conditions, normal body weight, fewer depressive symptoms, and fewer falls.
These descriptive results are consistent with research findings that life space is associated with health and mortality ,. Our paper investigated whether trends in our new mobility measure, MOVES, tracked with prevailing literature on mobility patterns. More in-depth analyses should explore the associations between sociodemographic and economic factors, MOVES and health outcomes. We acknowledge that MOVES has a number of limitations.
MOVES was available for only those who answered all included items, assumed that those under 65 have no fear of falling, and items were restricted to those previously measured in CCHS-HA. Other practitioners may benefit from adding in questions on size of social network, cognitive ability to read and understand signage, or other measures related to the conceptual frameworks and domains. We also do not know how MOVES would perform for an institutionalized population. Our example using MOVES to examine mobility patterns also has limitations. First, a Canadian sample may not generalize to other populations. Second, our analyses are not analytic and therefore only show descriptive bivariate patterns between MOVES and the sociodemographic and health variables. Further work would be needed, including age- and other adjustments to examine associations causally.
Finally, the sample used was a population-based sample of community dwelling middle-aged and older adults. It does not include people living in institutions, who may have lower mobility.
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We do not know how well the scale could be used to successfully differentiate between individuals or subgroups with very low levels of mobility. However, MOVES has numerous strengths across potential applications, and fills gaps created by limitations of other classification approaches. MOVES holds utility for researchers working in other population-based survey samples; since MOVES relies on common, pre-existing survey items, others with population surveys can derive a score to study holistic mobility. As such, this score is useful for benchmarking and tracking mobility across large geographic scales.
Some MOVES items might not be common to other surveys. Future studies might test whether substituting similar items can be made without compromising the performance of MOVES. Similar to the descriptive analyses we provide, MOVES can be used to ascertain differences across gender, socioeconomic status, geographies and other characteristics. MOVES may also be used in natural experiments to examine changes in mobility with policy shifts or infrastructure investments, although we were unable to test how sensitive MOVES is to change using this cross-sectional sample. Similarly, researchers can use MOVES to understand the association between broad mobility and health outcomes, including self-rated health and overall mortality.
Alternatively, MOVES could be used by policy makers and practitioners hoping to better understand mobility. MOVES provides insight on how well older adults are able to engage with their communities, and would enhance discussions around planning for driving cessation and maintaining mobility. Ultimately, MOVES represents the quantitative embodiment of evidence and conceptual frameworks of mobility.
By assigning numeric values to these concepts, it further enhances discourse between various stakeholders around supports for older adult mobility and opens new avenues of research. Grounded in frameworks and qualitative research that support conceptualizing mobility across physical, cognitive, transport and social domains, this study created a quantitative measurement tool (MOVES) for mobility that encompasses multiple domains. Descriptive data on MOVES in older adults from across Canada followed expected sociodemographic, economic, and health patterns of mobility levels. MOVES appears useful for research, surveillance, evaluation, and interventions around the broad factors influencing mobility in older adults. Future work could use MOVES to examine determinants, consequences and changes in of mobility for older adults across a range of setting and populations. Acknowledgements Statistics Canada thanks all participants for their valuable input and advice during the development of the 2008/2009 Canadian Community Health Survey― Healthy Aging. Consultations included stakeholders from Human Resources and Social Development Canada and provincial and territorial health ministries.
The authors would like to acknowledge the non-authors on the expert panel who assisted in development and refinement of MOVES: Callista Haggis, Thea Franke, Christine Voss, Dawn Mackey, and Suzanne Therrien. This work was undertaken in the University of British Columbia and the Simon Fraser University Statistics Canada Research Data Centres with the assistance of Lee Grenon and Lisa Oliver. Funding The survey content was developed by the Health Statistics Division at Statistics Canada in consultation with Health Canada, the Public Health Agency of Canada, and experts conducting the Canadian Longitudinal Study on Aging (CLSA), a major strategic initiative of the Canadian Institutes of Health Research. The addition of 5000 respondents aged 45 to 54 was funded by the CLSA. Research was supported by the Canadian Institutes of Health Research (CIHR) grant number F14–03087.
Hirsch is supported by the Population Research Training grant (T32 HD007168) and the Population Research Infrastructure Program (R24 HD050924) awarded to the Carolina Population Center at The University of North Carolina at Chapel Hill by the Eunice Kennedy Shriver National Institute of Child Health and Human Development. Sims-Gould is supported by a CIHR New Investigator award and a Michael Smith Foundation for Health Research Scholar award. Meghan Winters is supported by a Michael Smith Foundation for Health Research Scholar award. Ashe is supported by the Canada Research Chairs Program.
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The Sims 2: Apartment Life This expansion pack for The Sims 2 takes your Sims out of the suburbs and into a brand new busy apartment building! Adventure, fun, and drama await them as they explore their new neighborhood, mingle in coffee shops, visit a playground, meet new friends, and much more. Because there is now the ability to control multiple households in The Sims 2 Apartment Life, your Sims can live in happy harmony, or comical conflict. New social networks and character types, including artsy bohemians, sports fans, gadget-loving techies,spice up your Sims life. And, with a new reputation meter, your Sims can build up the respect they deserve from other Sims to be the most admired – or least-desired -Sim in the neighborhood. The better the reputation, the easier for your Sims to find the right friends to help them achieve their goals. PC News Aug 26, 2008 The Sims, an Electronic Arts Inc.
(NASDAQ: ERTS) Label, shipped The Sims 2 Apartment Life for the PC, and The Sims 2 Apartment Pets exclusively for the Nintendo DSTM to retail outlets nationwide. In both releases, players leave suburbia for the action and excitement of apartment life. The eighth expansion pack for The Sims 2 franchise, The Sims 2 Apartment Life delivers the drama and fun of apartment living as they move into a brand new building, explore their new neighborhood, mingle in coffee shops, meet new friends and much more. In The Sims 2 Apartment Pets, life in the apartment becomes a lot less lonely as a friendly, furry or fine-feathered friend offers companionship and delivers happiness and joy to Sims.
Both titles shipped to retail outlets in North America, European and Asia-Pacific regions and are available now! PC News Jun 5, 2008 The Sims, an Electronic Arts Inc. (NASDAQ: ERTS) Label, today announced the first details for The Sims 2 Apartment Life for the PC, and The Sims 2 Apartment Pets exclusively for the Nintendo DSTM. The Sims 2 Apartment Life brings the adventure, fun and drama of close quarters of apartment living to your Sims. Launching simultaneously, The Sims 2 Apartment Pets places your Sims in their very own apartment with a variety of pets. Also, just downstairs from your Sims apartment is their own pet spa where they must take good care of a variety of visiting animals, including their own personal pets.
Both titles are due for launch on August 22nd. Move into the perfect apartment: a spacious loft, a cozy place for a young couple, or a multi-bedroom flat-share with friends.
Mingle with Sims from all-new social groups: stylish socialites, artsy bohemians, sports jocks, gadget-collecting techies, or edgy gearheads. Take advantage of apartment life: form social networks to make new friends, advance their careers, or look for love. Build your Sim's new reputation meter: with a good reputation, your Sim can find the right friends to help them achieve their goals!.
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8 With The Sims 2: Apartment Life you'll give your sims the opportunity to leave their peaceful towns and move to an apartment in a large city. The Sims 2: Apartment Life adds a new neighborhood to the game, Belladonna Cove, with many apartments and other buildings such as grocery stores, coffee shops and even a trailer park. Also, as with other expansion sets, The Sims 2: Apartment Life also adds a bunch of new objects, building elements and furniture.
When you decide to move your sims to the city, they'll have to find an appropriate apartment and pay a weekly rent for it. Then it's time to start interacting with your neighbors. The Sims 2: Apartment Life adds more ways of social interaction to the sims menus, as well as new NPCs like roomates, butlers and landlords - which, in fact, come in really handy to fix house problems. Finally, another new element added to The Sims 2: Apartment Life is magic.
Your sims will be able to become witches or warlocks, with the possibility to choose their nature (from Infallibly Good to Atrociously Evil). These magic powers, though a bit out of context in my opinion, give you the ability to cast spells to obtain diverse enhancements. Tired of your quiet Sim neighborhood? Move to the big city and get ready for some action.